Neural network pruning is an essential approach for reducing the computational complexity of deep models so that they can be well deployed on resource-limited devices. Compared with conventional methods, the recently developed dynamic pruning methods determine redundant filters variant to each input instance which achieves higher acceleration. Most of the existing methods discover effective sub-networks for each instance independently and do not utilize the relationship between different inputs. To maximally excavate redundancy in the given network architecture, this paper proposes a new paradigm that dynamically removes redundant filters by embedding the manifold information of all instances into the space of pruned networks (dubbed as ManiDP). We first investigate the recognition complexity and feature similarity between images in the training set. Then, the manifold relationship between instances and the pruned sub-networks will be aligned in the training procedure. The effectiveness of the proposed method is verified on several benchmarks, which shows better performance in terms of both accuracy and computational cost compared to the state-of-the-art methods. For example, our method can reduce 55.3% FLOPs of ResNet-34 with only 0.57% top-1 accuracy degradation on ImageNet.

本论文提出了一种新的范式，通过将所有实例的流形信息嵌入到裁剪网络的空间中(称为ManiDP)，以动态地去除冗余的过滤器以最大化挖掘给定网络架构中的冗余。在几个基准测试中验证了所提出的方法的有效性，在精度和计算成本方面显示出与现有技术方法相比更好的性能可将ResNet-34的FLOP降低55.3％，且仅仅减少0.57％的Top-1精度，ImageNet。

流形正则化动态网络剪枝